SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 2171–2180 of 15113 papers

TitleStatusHype
Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot—0
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem—0
Fairness Incentives in Response to Unfair Dynamic Pricing—0
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation—0
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy DataCode1
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories—0
Multi-view Disentanglement for Reinforcement Learning with Multiple CamerasCode0
An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems—0
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified